Heretofore, conventional wisdom has held that it is impossible to predict the stock market with any degree of accuracy. The same conventional wisdom has also been directed to other financial and economic markets and institutions albeit perhaps to a lesser degree.
Yet despite such conventional wisdom many other people have tried to develop mathematical models that predict stock market behavior. What almost all of these other mathematical models have in common is that they are based on economic inputs—interest rates, assumptions about supply and demand, the expansion of the economy, and the like economic factors and indicators. Many of these models are tuned and validated against actual past historical data, and then are used to try to predict future data or behavior. Unfortunately, no known model of this type has worked sufficiently well to overturn the general opinion that the stock market cannot be predicted with any useful degree of accuracy. And, in particular no known conventional model has permitted stock market prediction in a time frame that permits stock market trading decisions to be usefully made based on such predictions.
Besides the people using mathematical models to predict the stock market, there are also mutual find managers, analysts, brokers, day-traders, and other financial services professionals, who make their living (or try to make a living) by trying to make accurate judgments about what the market is doing or about to do. That most of these professionals are not very accurate, is perhaps best illustrated by the observation that just buying the Standard & Poors (S&P) 500 would allow an investor to outperform 80% of these professional money managers and financial advisors. In fact, the Wall Street Journal and other newspapers have great fun with contests that pit the “dartboard” (random stock picks) against the top analysts—illustrating that the professionals are often worse than random chance at picking stocks.
Another approach is to provide a website that solicits the stock picks of individual investors. The websites rate these individual investors based on their performance picking stocks on the website, much the same way the newspapers have rated analysts. These sites are attempting to discover new experts among the masses of everyday investors. These sites are not directed to predicting particular stock values at particular times and are more nearly directed to longer term performance picks.
Other websites and models and methods associated with such websites have expert analysts on staff who sort through the information generated by the masses, and try to find nuggets that help drive investment decisions. These sites are attempting to leverage the information gathering capabilities of Internet users to do the research for their own in-house experts.
Both web-based approaches rely on conventional wisdom, which says that individual experts (either in-house experts or experts that have been rated by the website) but not ordinary non-expert investors are the key to making good investment decisions. Although use of the Internet makes the approaches seem to be high technology approaches, really the paradigm is an old one: experts will make the best guesses.
The idea that the collective intelligence of many individual investors could outperform the experts has not heretofore been considered as it seemed-counter-intuitive to most investment professionals. For example, recently a stock trader with twenty years experience and a seat on a major exchange opined that using the collective wisdom of many investors did not seem like a fruitful approach to forecasting stock prices. His reasoning was that if many people believed something, this belief was probably already factored into the prices and you couldn't make money by acting on what lots of people believed.
The current conventional wisdom when it comes to forecasting stock prices can pretty much be summarized by the following two themes: (i) it probably can't be done reliably, and (ii) if it can be done at all, it requires specialized experts. The current state of forecasting technology reflects this wisdom where attempts are made to identify and utilize one or a small group of experts, but none of these systems do very well compared to chance.